| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 45 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 86.45% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1476 | | totalAiIsmAdverbs | 4 | | found | | 0 | | | 1 | | | 2 | | adverb | "passionately" | | count | 1 |
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| | highlights | | 0 | "very" | | 1 | "slightly" | | 2 | "passionately" |
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| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 66.12% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1476 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "familiar" | | 1 | "rhythmic" | | 2 | "weight" | | 3 | "scanning" | | 4 | "flicked" | | 5 | "tension" | | 6 | "velvet" | | 7 | "silence" | | 8 | "gloom" | | 9 | "measured" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 65 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 65 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 98 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1476 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 82.36% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 961 | | uniqueNames | 17 | | maxNameDensity | 1.35 | | worstName | "Liam" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Liam" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Golden | 1 | | Empress | 1 | | Soho | 2 | | Oxfords | 1 | | London | 1 | | Brendan | 1 | | Carter | 1 | | Cardiff | 1 | | Liam | 13 | | French | 1 | | Rory | 9 | | Silas | 8 | | Prague | 1 | | Berlin | 1 | | Macallan | 2 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Oxfords" | | 3 | "Brendan" | | 4 | "Carter" | | 5 | "Liam" | | 6 | "Rory" | | 7 | "Silas" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Cardiff" | | 3 | "Prague" | | 4 | "Berlin" |
| | globalScore | 0.824 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | glossingSentenceCount | 1 | | matches | | 0 | "as if addressing a magistrate, told the entire story" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1476 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 98 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 58 | | mean | 25.45 | | std | 18.93 | | cv | 0.744 | | sampleLengths | | 0 | 64 | | 1 | 43 | | 2 | 6 | | 3 | 17 | | 4 | 10 | | 5 | 52 | | 6 | 6 | | 7 | 34 | | 8 | 1 | | 9 | 25 | | 10 | 1 | | 11 | 61 | | 12 | 45 | | 13 | 7 | | 14 | 20 | | 15 | 5 | | 16 | 29 | | 17 | 34 | | 18 | 12 | | 19 | 4 | | 20 | 2 | | 21 | 51 | | 22 | 6 | | 23 | 12 | | 24 | 28 | | 25 | 28 | | 26 | 49 | | 27 | 20 | | 28 | 47 | | 29 | 3 | | 30 | 25 | | 31 | 3 | | 32 | 21 | | 33 | 46 | | 34 | 9 | | 35 | 11 | | 36 | 1 | | 37 | 48 | | 38 | 12 | | 39 | 24 | | 40 | 60 | | 41 | 28 | | 42 | 32 | | 43 | 5 | | 44 | 68 | | 45 | 49 | | 46 | 39 | | 47 | 42 | | 48 | 34 | | 49 | 9 |
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| 94.47% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 65 | | matches | | 0 | "been hollowed" | | 1 | "was measured" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 143 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 98 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 973 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.018499486125385406 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.008221993833504625 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 98 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 98 | | mean | 15.06 | | std | 9.25 | | cv | 0.614 | | sampleLengths | | 0 | 19 | | 1 | 28 | | 2 | 17 | | 3 | 5 | | 4 | 26 | | 5 | 12 | | 6 | 6 | | 7 | 4 | | 8 | 13 | | 9 | 10 | | 10 | 13 | | 11 | 22 | | 12 | 17 | | 13 | 6 | | 14 | 22 | | 15 | 2 | | 16 | 10 | | 17 | 1 | | 18 | 6 | | 19 | 19 | | 20 | 1 | | 21 | 21 | | 22 | 17 | | 23 | 11 | | 24 | 12 | | 25 | 26 | | 26 | 7 | | 27 | 12 | | 28 | 7 | | 29 | 6 | | 30 | 14 | | 31 | 5 | | 32 | 19 | | 33 | 8 | | 34 | 2 | | 35 | 9 | | 36 | 25 | | 37 | 12 | | 38 | 4 | | 39 | 2 | | 40 | 25 | | 41 | 26 | | 42 | 6 | | 43 | 12 | | 44 | 28 | | 45 | 7 | | 46 | 21 | | 47 | 24 | | 48 | 25 | | 49 | 20 |
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| 70.07% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.4387755102040816 | | totalSentences | 98 | | uniqueOpeners | 43 | |
| 53.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 62 | | matches | | 0 | "Only Brendan Carter back in" |
| | ratio | 0.016 | |
| 71.61% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 62 | | matches | | 0 | "His left leg caught with" | | 1 | "She spun the tumbler." | | 2 | "Her left sleeve rode up," | | 3 | "He wore a bespoke overcoat" | | 4 | "He closed the distance between" | | 5 | "His posture had stiffened into" | | 6 | "He looked at her faded" | | 7 | "His smile faltered at the" | | 8 | "His hazel eyes flicked from" | | 9 | "He gestured to the empty" | | 10 | "He wrapped his manicured fingers" | | 11 | "It wasn't a question" | | 12 | "He took a slow sip" | | 13 | "His voice dropped an octave," | | 14 | "He looked around the dimly" | | 15 | "He looked down at her" | | 16 | "She turned on the stool" | | 17 | "He set the glass down" | | 18 | "He swept a hand through" | | 19 | "He was twenty-six, a year" |
| | ratio | 0.371 | |
| 16.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 55 | | totalSentences | 62 | | matches | | 0 | "Rory rested her chin in" | | 1 | "Silas moved behind the taps." | | 2 | "His left leg caught with" | | 3 | "The silver signet ring on" | | 4 | "She spun the tumbler." | | 5 | "Her left sleeve rode up," | | 6 | "The front door swung open," | | 7 | "Streetlamp glare and the hiss" | | 8 | "He wore a bespoke overcoat" | | 9 | "Rory’s fingers froze on the" | | 10 | "The man stepped under the" | | 11 | "The polished leather of his" | | 12 | "Nobody used that name in" | | 13 | "He closed the distance between" | | 14 | "His posture had stiffened into" | | 15 | "He looked at her faded" | | 16 | "His smile faltered at the" | | 17 | "Silas set the polished glass" | | 18 | "His hazel eyes flicked from" | | 19 | "Liam didn't look at Silas," |
| | ratio | 0.887 | |
| 80.65% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 62 | | matches | | 0 | "Now, a gold watch peeked" |
| | ratio | 0.016 | |
| 67.67% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 4 | | matches | | 0 | "The man stepped under the amber glow of the wall sconces, his eyes scanning the monochrome photographs before landing on the bar." | | 1 | "The cut of his jaw, the way he held his chin high as if addressing a magistrate, told the entire story." | | 2 | "Liam flinched, the polished barrister mask cracking to reveal the anxious twenty-year-old who used to hyperventilate before oral arguments." | | 3 | "Liam opened his mouth, his jaw working for a rebuttal, searching for a clause or a loophole to win the point." |
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| 75.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 1 | | matches | | 0 | "She turned on, her blue eyes sharp and cold" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |